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Merge branch 'master' into t5

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Thomas Wolf
2019-12-13 16:03:32 +01:00
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docs/source/pretrained_models.rst
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@@ -61,6 +61,24 @@ Here is the full list of the currently provided pretrained models together with
| | ``bert-base-german-dbmdz-uncased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
| | | | Trained on uncased German text by DBMDZ |
| | | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__). |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``bert-base-japanese`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
| | | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece. |
| | | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization. |
| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``bert-base-japanese-whole-word-masking`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
| | | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized with MeCab and WordPiece. |
| | | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization. |
| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``bert-base-japanese-char`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
| | | | Trained on Japanese text. Text is tokenized into characters. |
| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``bert-base-japanese-char-whole-word-masking`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
| | | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters. |
| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| GPT | ``openai-gpt`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
| | | | OpenAI GPT English model |
@@ -169,35 +187,35 @@ Here is the full list of the currently provided pretrained models together with
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| ALBERT | ``albert-base-v1`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters |
| | | | ALBERT base model |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``albert-large-v1`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters |
| | | | ALBERT large model |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``albert-xlarge-v1`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters |
| | | | ALBERT xlarge model |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``albert-xxlarge-v1`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters |
| | | | ALBERT xxlarge model |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``albert-base-v2`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters |
| | | | ALBERT base model with no dropout, additional training data and longer training |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``albert-large-v2`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters |
| | | | ALBERT large model with no dropout, additional training data and longer training |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``albert-xlarge-v2`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters |
| | | | ALBERT xlarge model with no dropout, additional training data and longer training |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``albert-xxlarge-v2`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters |
| | | | ALBERT xxlarge model with no dropout, additional training data and longer training |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| T5 | ``t5-small`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
| | | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint |